SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 1971–1980 of 3874 papers

TitleStatusHype
Cross-domain heterogeneous residual network for single image super-resolutionCode0
SelfReformer: Self-Refined Network with Transformer for Salient Object DetectionCode1
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
Reliability-based Mesh-to-Grid Image Reconstruction—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video RestorationCode1
Combining Contrastive and Supervised Learning for Video Super-Resolution DetectionCode0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified